Faa di Bruno's formula for Gateaux differentials and interacting stochastic population processes

Abstract

The problem of estimating interacting systems of multiple objects is important to a number of different fields of mathematics, physics, and engineering. Drawing from a range of disciplines, including statistical physics, variational calculus, point process theory, and statistical sensor fusion, we develop a unified probabilistic framework for modelling systems of this nature. In order to do this, we derive a new result in variational calculus, Faa di Bruno's formula for Gateaux differentials. Using this result, we derive the Chapman-Kolmogorov equation and Bayes' rule for stochastic population processes with interactions and hierarchies. We illustrate the general approach through case studies in multi-target tracking, branching processes and renormalization.

Cited by in corpus (6)

• A second-order PHD filter with mean and variance in target number
• Deep neural networks on diffeomorphism groups for optimal shape reparameterization
• Single-cluster PHD filter methods for joint multi-object filtering and parameter estimation
• Bayesian filtering for multi-object systems with independently generated observations
• Deconvolution of point processes
• Regional variance for multi-object filtering